Bottom Line:
Potential metabolic switches between eleven dominant members were mainly observed for acetate, hydrogen, and ethanol metabolisms.These results have enabled the estimation of a multi-species metabolic network and the associated short-term responses to EET stimuli that induce changes to metabolic flow and cooperative or competitive microbial interactions.This systematic meta-omics approach represents a next step towards understanding complex microbial roles within a community and how community members respond to specific environmental stimuli.

ABSTRACTMicroorganisms almost always exist as mixed communities in nature. While the significance of microbial community activities is well appreciated, a thorough understanding about how microbial communities respond to environmental perturbations has not yet been achieved. Here we have used a combination of metagenomic, genome binning, and stimulus-induced metatranscriptomic approaches to estimate the metabolic network and stimuli-induced metabolic switches existing in a complex microbial biofilm that was producing electrical current via extracellular electron transfer (EET) to a solid electrode surface. Two stimuli were employed: to increase EET and to stop EET. An analysis of cell activity marker genes after stimuli exposure revealed that only two strains within eleven binned genomes had strong transcriptional responses to increased EET rates, with one responding positively and the other responding negatively. Potential metabolic switches between eleven dominant members were mainly observed for acetate, hydrogen, and ethanol metabolisms. These results have enabled the estimation of a multi-species metabolic network and the associated short-term responses to EET stimuli that induce changes to metabolic flow and cooperative or competitive microbial interactions. This systematic meta-omics approach represents a next step towards understanding complex microbial roles within a community and how community members respond to specific environmental stimuli.

f1: Scheme for the analytical approach used to describe the microbial networks in a complex EET-active community.(Step 1) Enrichment of an EET-active microbial ecosystem; (Step 2) Metagenomic sequencing and analysis of the community; (Step 3) Bin-genome association by contig clustering. Each cluster indicates a Bin-genome of a community member, which includes coding sequences for cell activity and available metabolic pathways; (Step 4) stimulus-induced metatranscriptomics involving the application of a specific EET-condition via stimulus addition and biofilm sampling with DNA and mRNA extraction; (Step 5) Metatranscriptomic sequencing and subsequent comparative analyses of gene expression profiles of the whole community and each community member, which are executed via marker gene sets correlated with important “cell activity” and “metabolic” functions; (Step 6) Construction of the community metabolic network for the dominant microbes within the community.

Mentions:
Figure 1 summarizes our newly-developed approach for describing metabolic networks within the complex EET-active community. This approach addresses microbial activity as well as cooperative, competitive, or neutral metabolic interactions between dominant microbes within the community as described in Fig. 1 step 6.

f1: Scheme for the analytical approach used to describe the microbial networks in a complex EET-active community.(Step 1) Enrichment of an EET-active microbial ecosystem; (Step 2) Metagenomic sequencing and analysis of the community; (Step 3) Bin-genome association by contig clustering. Each cluster indicates a Bin-genome of a community member, which includes coding sequences for cell activity and available metabolic pathways; (Step 4) stimulus-induced metatranscriptomics involving the application of a specific EET-condition via stimulus addition and biofilm sampling with DNA and mRNA extraction; (Step 5) Metatranscriptomic sequencing and subsequent comparative analyses of gene expression profiles of the whole community and each community member, which are executed via marker gene sets correlated with important “cell activity” and “metabolic” functions; (Step 6) Construction of the community metabolic network for the dominant microbes within the community.

Mentions:
Figure 1 summarizes our newly-developed approach for describing metabolic networks within the complex EET-active community. This approach addresses microbial activity as well as cooperative, competitive, or neutral metabolic interactions between dominant microbes within the community as described in Fig. 1 step 6.

Bottom Line:
Potential metabolic switches between eleven dominant members were mainly observed for acetate, hydrogen, and ethanol metabolisms.These results have enabled the estimation of a multi-species metabolic network and the associated short-term responses to EET stimuli that induce changes to metabolic flow and cooperative or competitive microbial interactions.This systematic meta-omics approach represents a next step towards understanding complex microbial roles within a community and how community members respond to specific environmental stimuli.

ABSTRACTMicroorganisms almost always exist as mixed communities in nature. While the significance of microbial community activities is well appreciated, a thorough understanding about how microbial communities respond to environmental perturbations has not yet been achieved. Here we have used a combination of metagenomic, genome binning, and stimulus-induced metatranscriptomic approaches to estimate the metabolic network and stimuli-induced metabolic switches existing in a complex microbial biofilm that was producing electrical current via extracellular electron transfer (EET) to a solid electrode surface. Two stimuli were employed: to increase EET and to stop EET. An analysis of cell activity marker genes after stimuli exposure revealed that only two strains within eleven binned genomes had strong transcriptional responses to increased EET rates, with one responding positively and the other responding negatively. Potential metabolic switches between eleven dominant members were mainly observed for acetate, hydrogen, and ethanol metabolisms. These results have enabled the estimation of a multi-species metabolic network and the associated short-term responses to EET stimuli that induce changes to metabolic flow and cooperative or competitive microbial interactions. This systematic meta-omics approach represents a next step towards understanding complex microbial roles within a community and how community members respond to specific environmental stimuli.